Main roads and land cover shaped the genetic structure of a Mediterranean island wild boar population

Abstract Patterns of genetic differentiation within and among animal populations might vary due to the simple effect of distance or landscape features hindering gene flow. An assessment of how landscape connectivity affects gene flow can help guide management, especially in fragmented landscapes. Our objective was to analyze population genetic structure and landscape genetics of the native wild boar (Sus scrofa meridionalis) population inhabiting the island of Sardinia (Italy), and test for the existence of Isolation‐by‐Distance (IBD), Isolation‐by‐Barrier (IBB), and Isolation‐by‐Resistance (IBR). A total of 393 Sardinian wild boar samples were analyzed using a set of 16 microsatellite loci. Signals of genetic introgression from introduced non‐native wild boars or from domestic pigs were revealed by a Bayesian cluster analysis including 250 reference individuals belonging to European wild populations and domestic breeds. After removal of introgressed individuals, genetic structure in the population was investigated by different statistical approaches, supporting a partition into five discrete subpopulations, corresponding to five geographic areas on the island: north‐west (NW), central west (CW), south‐west (SW), north‐central east (NCE), and south‐east (SE). To test the IBD, IBB, and IBR hypotheses, we optimized resistance surfaces using genetic algorithms and linear mixed‐effects models with a maximum likelihood population effects parameterization. Landscape genetics analyses revealed that genetic discontinuities between subpopulations can be explained by landscape elements, suggesting that main roads, urban settings, and intensively cultivated areas are hampering gene flow (and thus individual movements) within the Sardinian wild boar population. Our results reveal how human‐transformed landscapes can affect genetic connectivity even in a large‐sized and highly mobile mammal such as the wild boar, and provide crucial information to manage the spread of pathogens, including the African Swine Fever virus, endemic in Sardinia.


| INTRODUC TI ON
Land-use changes can strongly affect the degree of landscape permeability to animal movement and impact genetic differentiation between and within populations of the same species (Lowe & Allendorf, 2010). Moreover, ecological barriers can lead to a disjunction and, sometimes, a complete isolation of subpopulations.
The shortage of permeable pathways and the presence of ecological barriers might limit gene flow between subpopulations and contribute to a loss of genetic diversity by genetic drift and to an increase of inbreeding ). In the last two decades, several analytical approaches have been developed to infer microevolutionary processes driven by habitat fragmentation and human infrastructures, giving rise to the discipline called landscape genetics (Manel et al., 2003;Storfer et al., 2010). Landscape genetic studies integrate population genetics, spatial analyses, and landscape ecology to test hypotheses about how environmental features influence population genetic structure and gene flow (Storfer et al., 2007).
Since urban, suburban development and road network extension are among the primary causes of habitat fragmentation, this analysis can be helpful in planning management practices for species conservation (Holderegger & Di Giulio, 2010;Kimming et al., 2020;Serieys et al., 2014). In fact, several studies indicated that landscape features can shape the gene flow within populations of large mammals (Castilho et al., 2011;Coulon et al., 2006;Pérez-Espona et al., 2008;Rutten et al., 2019;Sharma et al., 2013, Weckworth et al., 2013, and pointed out that assessing levels of population connectivity is particularly important to inform management practices. Urbanization and development of large networks of transport infrastructures have rapidly increased in Europe. The impact of anthropogenic barriers and habitat fragmentation on gene flow was investigated in different wild ungulates (Coulon et al., 2006;Dellicour et al., 2019;Frantz et al., 2012;Hepenstrick et al., 2012;Renner et al., 2016;Šprem et al., 2013). However, establishing the real impact of such barriers is challenging, since they could have various levels of permeability depending on the species behavioral characteristics. Frantz et al. (2012) showed how the presence of a motorway could differently affect two ungulate species in Belgium, acting as a barrier for the red deer (Cervus elaphus), while apparently not disturbing wild boars (Sus scrofa).
The wild boar is an ungulate species native to Europe (Apollonio et al., 2010) and one of the widest-ranging mammals in the world, adaptable to almost any type of environment. Climate represents the main limiting factor for wild boars, through its effect on physiology and metabolism, or through its indirect effect on food availability and accessibility (Geisser & Reyer, 2005;Melis et al., 2006;Vetter et al., 2015). In the last decades, wild boar populations in Europe have been increasing in numbers and distribution (Massei et al., 2015), causing conflicts with humans, also linked to public health. A major threat arises from the infection of wild boar populations with African swine fever (ASF) virus, which has been endemic in Sardinia since 1978 (Jurado et al., 2018) and spreading within the EU since 2014 (EFSA Panel on Animal Health & Welfare, 2018), with a recent outbreak recorded in north-western Italy. Spillover of ASF from free-ranging wild boar to farmed domesticated pigs has been detrimental to the domestic pig industry (Bosch et al., 2020;Reiner et al., 2021). Thus, understanding the spatial behavior of wild boar is essential for managing ASF in the free-ranging wild boar population.
Wild boars are characterized by a variable use of space (Keuling et al., 2008), regardless of the habitat occupied. Wild boar dispersal takes place between 11 and 16 months of age and usually covers limited distances (<20 km, Keuling et al., 2010;Truvè & Lemel, 2003).
Dispersal patterns are influenced by various factors such as population density, habitat structure and quality, and climate (Dardaillon & Bougnon, 1987;Keuling et al., 2010). For instance, wild boar is known to modify its activity and spatial patterns in relation to human disturbance. If undisturbed, wild boars tend to be active during the day, while under hunting pressure and high human disturbance they shift their activity to nocturnal (Brivio et al., 2017;Podgórski et al., 2013). Nevertheless, in some places, wild boars adapt well to human presence and infrastructure in urban areas (Cahill et al., 2012;Osashi et al., 2013).
Our study is focused on the wild boar population inhabiting the Mediterranean island of Sardinia (Italy). Sardinian wild boar, a dwarf form of the European wild boar, is believed to have originated during the Neolithic following a human introduction from the mainland (Albarella et al., 2006). It is currently classified as a distinct subspecies (Sus scrofa meridionalis Major 1883), based on both morphological and genetic evidence, as it is characterized by a relevant genetic differentiation, due to its long-lasting isolation, reported by Scandura et al. (2008), Scandura et al. (2009), Scandura et al. (2011 and Iacolina et al. (2016). However, outdoor pig farming practices in some areas and the uncontrolled release of continental wild boars for hunting purposes have jeopardized the endemic genetic diversity of the population. Scandura et al. (2011), indeed, detected substantial levels of genetic introgression from domestic pigs and continental wild boar, and a relevant population genetic structure into three subpopulations (east, north-west, south-west), suggesting that the sharp east-west genetic differentiation could not be explained by isolation-by-distance only, and that landscape features could play an important role.
Here, we analyzed the Sardinian wild boar population, expanding sampling and increasing the number of genetic markers, with the aim to evaluate the genetic structure suggested in the previous studies, in relation to natural and anthropogenic environmental variables that could act as barriers, preventing gene shuffling

T A X O N O M Y C L A S S I F I C A T I O N
Landscape ecology; Population genetics among subpopulations. Isolation-by-Distance (IBD), Isolation-by-Barrier (IBB), and Isolation-by-Resistance (IBR) were tested using a landscape genetic approach by comparing alternative landscape resistances, as suggested by Frantz et al. (2012). Landscape permeability to wild boar movements was tested by combining different genetic clustering methods with landscape resistance modeling.

| Study area
The island of Sardinia is the second largest in the Mediterranean Sea (24,090 km 2 ). Human population density is relatively low for Europe (around 68 inhabitants/km 2 ) and people mainly live in major cities and along the coasts, while small villages and large uninhabited areas characterize the interior. Climate is Mediterraneantemperate at low elevations and along the coast, more continental inland and at higher elevations. Temperature is mild and relatively constant throughout the year (on average 18°C, ranging between a mean of 7°C in winter and 25°C in summer). Annual precipitations range from less than 400 mm in the dry south to 1500 mm in the eastern mountains. Such climatic conditions are suitable for wild boar all over the island.
The island is relatively dry, and some rivers may be reduced to streams in summer. A single small natural lake and several artificial basins are also present, as well as ponds and lagoons along the coasts. Mountains occupy only 13.6% of the territory and are mainly concentrated in the central-eastern part of the island, reaching a maximum elevation of 1834 m a.s.l. Vegetation is mainly represented by Mediterranean maquis, deciduous forest, grassland, and pastures. Plateaus and flatlands occupy 18.5% of the island territory, the main one being represented by the Campidano plain in the south-west, a human-modified landscape dominated by cultivations, especially cereal crops, orchards, and vineyards.
The wild boar is widespread all over the island, occurring in various habitats due to its ecological plasticity, being rare only in the Campidano plain. Estimates of population size are affected by large confidence intervals (a minimum of 20,000 was estimated in 2010), and, based on habitat suitability analyses, higher densities were expected to occur in the central and northern part of the island (Regione Autonoma della Sardegna, 2012).
Human activities and infrastructures potentially have a strong impact on the presence of wild boar. Roads and railway networks, encountered by large mammals, may become an effective barrier, limiting species dispersal, if associated with physical barriers or with high traffic (Kimming et al., 2020). Few main roads with the mentioned features occur in Sardinia: for instance, the SS131 "Carlo Felice", a motorway with 4 lanes and very few crossing points for wildlife. It crosses the island from south to north along more than 200 km connecting the two major cities, Cagliari and Sassari.   Rousset, 1995). Tests for HWE employed the Markov chain method proposed by Guo and Thompson (2002), with the following chain parameters: 10,000 dememorizations, 100 batches, and 10,000 iterations.

| Microsatellite and population genetic analysis
Deviations from LE were tested for each pair of loci. Significance levels were lowered, accounting for the number of multiple tests by the sequential Bonferroni procedure (Rice, 1989). Allele frequencies and genetic diversity at the 16 loci, observed (H O ) and expected (H E ) heterozygosity, mean number of alleles per locus (A), and F IS were computed in GENETIX v. 4.05 (Belkhir, 2004).
To ensure that related individuals in the dataset did not bias genetic structure analysis, GENALEX v. 6 (Peakall & Smouse, 2006) and ML RELATE (Kalinowski et al., 2006) were used to estimate pairwise relatedness (QG estimator, Queller & Goodnight, 1989) and to identify the most likely parent-offspring and full-sibling pairs in the starting dataset. Only one individual of each pair/group of related individuals was then retained.
The occurrence of imported exotic boars and the signature of genetic introgression from continental populations (Italian peninsula or central Europe) and from domestic pigs in Sardinia was already reported by Scandura et al. (2011). As distortions in allele frequencies due to recent introgressive hybridization can locally alter patterns of genetic structure, we preliminarily screened all Sardinian genotypes to detect and remove individuals showing non-negligible signals of human-mediated introgression (see below). For this purpose, Sardinian wild boar genotypes were compared with 100 reference wild boar from different European countries (20 samples from Spain, and 10 samples from France, Austria, Belarus, Croatia, Estonia, Hungary, Luxembourg, and Poland respectively), 50 Italian mainland wild boar, and 100 domestic pigs from Sardinia, including commercial and local free-ranging individuals. These samples, partially used in previous studies (Canu et al., 2014(Canu et al., , 2018Scandura et al., 2011), had been genotyped using the same protocols as the Sardinian ones.
To detect introgressed individuals, we performed 10 independent Markov chain Monte Carlo (MCMC) runs simulating a number of subpopulations (K) ranging from 1 to 10, with the following settings: admixture model, use population information, correlated allele frequencies, 500,000 burn-in and 500,000 iterations of data collection. The optimal value of K was determined using the ΔK method of Evanno et al. (2005) implemented in Structure Harvester (Earl & VonHoldt, 2012). Accordingly, each individual sampled in Sardinia was assessed in relation to the possible genetic introgression from other wild and domestic populations. Individual admixture was evaluated by referring to the q-values obtained in the best run with the selected K-value. To be conservative, only individuals showing >90% cumulative membership to the Sardinian clusters were retained for further analyses (see also Frantz et al., 2013). F I G U R E 1 Map of Sardinia showing the geographic locations of the Sardinian wild boar samples and the different land use classes used for modeling. Main roads in the island are shown STRUCTURE was run again to infer population clustering by analyzing the clean dataset of Sardinian wild boar. A total of 10 independent MCMC runs were performed, simulating a number of subpopulations (K) ranging from 1 to 10, with settings: admixture model, no population information, correlated allele frequencies, 500,000 burn-in and 500,000 iterations of data collection. Again, the optimal K-value was chosen according to the ΔK statistics in Structure Harvester (Earl & VonHoldt., 2012). Pophelper (Francis, 2017) was used to edit STRUCTURE results, visualize outputs and produce the final plots.
To confirm the structuring pattern, a Principal Component Analysis (PCA) was also performed using Adegenet package in R v, 4.0.2 (Jombart, 2008;R Core Team, 2020) to detect differentiation among non-introgressed genotypes in relation to their assigned subpopulation. For this purpose, the purged dataset of "pure" Sardinian wild boar was used, labeling individuals with q ≥ 0.6 to a specific Bayesian cluster (from the previous STRUCTURE analysis) as belonging to the corresponding subpopulation.
Genotypes were plotted in a two-dimensional space based on their genetic proximity. Pairwise Rousset's a r genetic distance (Rousset, 2000), shown to be among the most accurate metrics for landscape genetic approaches (Shirk et al., 2017), was computed between Sardinian wild boar samples using SpaGeDi ver.

| Landscape genetics analyses
Three potential drivers of genetic variation patterns observed in the Sardinian wild boar population were tested: Isolation-By-Distance (IBD), Isolation-By-Barrier (IBB), and Isolation-By-Resistance (IBR).
To assess the relevance of each driver, the resistance optimization process described by Peterman et al. (2014) was implemented using the package ResistanceGA (Peterman, 2018) in R v. 4.0.2 (R Core Team, 2020) within the MARCONI HPC System at CINECA (www.hpc.cineca.it/). This approach uses stochastic search algorithms that solve optimization problems by simulating natural selection processes (Scrucca, 2013) to find the resistance surface values that best explain the observed genetic distances. When applied to categorical surfaces (e.g., land-cover or barrier maps), the process iteratively creates resistance surfaces assigning new set of resistance values to each category of the map, calculates pairwise ecological (cost) distances from the resistance surfaces, and regresses genetic against ecological distances by fitting linear mixed-effects models with a maximum likelihood population effects parameterization (MLPE).
The MLPE is used to control for non-independence among pairwise data (Clarke et al., 2002) and has been recognized as the best performing model in landscape genetic model selection (Shirk et al., 2018). Model performance was assessed through AICc values and optimization proceeded until no additional AICc improvement was obtained. We calculated cost distances among all wild boar sampling locations obtained from the dataset purged from related and/or introgressed individuals (n = 270) using Circuitscape 5.0 implemented in Julia (Hall et al., 2021;McRae et al., 2008McRae et al., , 2016. We used the pairwise Rousset's a r genetic distance as the dependent variable.
To test the IBB hypothesis, we optimized a binary grid surface with a 500 × 500 m resolution where cells crossed by main roads had a value equal to 1 while all other cells had a value equal to 0.
Main roads were identified as those with an Average Daily Traffic (ADT) higher than one standard deviation of the mean ADT from all the sampling stations in Sardinia (national traffic monitoring network, http://dati.mit.gov.it/catal og/datas et/traff ico-giorn aliero-medio-anas). To test the IBR hypothesis, we optimized a categorical land-cover grid surface. Land-cover data were obtained from a digital map of Sardinia (Carta della Natura Regione Sardegna, 1:50,000 resolution, Camarda et al., 2015) rasterized at a 500 × 500 m pixel resolution. The original 93 land-cover classes were reclassified into 9 categories: broadleaved forests, coniferous forests, Mediterranean maquis, simple arable lands, permanent crops, meadows and pastures, beaches and rocky areas, water bodies, and urban areas.
Moreover, to have a better representation of the environmental complexity that wild boars face while moving through the landscape, we overlapped the grid surface representing main roads to the landcover surface, considering main roads as a further land-cover type.
Thus, IBR simultaneously accounted for the effect of land-covers and main roads on gene flow. In addition, we assessed Euclidean distance alone (IBD hypothesis) as well as an intercept-only null model.
The relative performances of the IBD model and the optimized IBB and IBR models were evaluated both considering the ΔAICc to the best model and the conditional R 2 value (R 2 c).
We used the optimized resistance generated by the model with most support to create a current density map of whole Sardinia by following the approach of Koen et al. (2014). We designed a 45-km-wide buffer around our study area, roughly 20% of the length and 40% of the width of the study area, then randomly selected 100 nodes around the perimeter of the buffer and used Circuitscape to connect all node pairs. We then removed the buffer and obtained a current density map showing the probability of using each grid cell by free-ranging wild boars.
In order to integrate information coming from the population structure and landscape resistance analyses, we tested whether the observed genetic clustering of the Sardinian wild boar population into subpopulations can be explained by the landscape resistance among them. Specifically, we regressed the ecological distances calculated using Circuitscape from the optimized resistance surface of the best model on a dichotomous categorical variable that classifies a pair of locations as belonging to the same or to different clusters and the Euclidean distance between them. The latter was included to account for the effect of spatial arrangement of locations in determining genetic clustering and was centered and scaled. Locations that were not assigned to a cluster were removed from the regression model.

| Microsatellite diversity
The total number of alleles detected in the Sardinian wild boar sample was 154, ranging from 6 to 16 per locus and an average of 9.63 ± 3.18 (standard deviation, SD) per locus. Missing alleles represented 2.17% of the dataset. MICRO-CHECKER did not find any scoring error in the dataset or evidence of allele dropout. Properties of the 16 microsatellite loci used in this study and the variability observed at each locus are shown in Table 1 to represent full-siblings or parent/offspring. Therefore, they were removed from the dataset to obtain a cleaned pool of 318 unrelated individuals to perform the following analyses.

| Identification of introgressed individuals
At K = 5 (or higher) the Bayesian analysis in STRUCTURE sharply distinguished the main source populations in the overall sample of 568 individuals (250 reference individuals from mainland Italy, rest of Europe and domestic pigs, and 318 Sardinian wild boar). However, in order to identify individuals with a clear signature of genetic introgression in Sardinia, we selected K = 4 as it showed a higher support than K = 5 (ΔK method, see Figure 2 and Appendix S1), with cluster I identifying European and Italian wild boar, cluster III associated with domestic pigs, and Sardinian wild boar mainly assigned to two clusters (II and IV). Hence, to conservatively assess which individual was a possible recent immigrant/hybrid, we applied the threshold of 0.9 to the sum of q-values referred to the two Sardinian clusters (qII + IV). For further analyses we thus removed from the dataset a total of 48 (15%) individuals showing introgression from continental wild boar or domestic pigs, and obtained a final purged dataset of 270 Sardinian wild boars.

| Genetic structure
The Bayesian analysis performed in STRUCTURE to highlight the genetic structure of the Sardinian wild boar population (purged dataset) detected a partition in two clusters (K = 2), as the most likely, but local maxima were detected also at K = 5 and K = 8 (ΔK method, Appendix S1). At K = 2, data suggested a partition between wild boar samples from the west (central and south-west) and wild boars from the rest of the island (north and east), with the main discontinuity between the two clusters apparently represented by the SS131. At K = 5, five subpopulations were clearly identified (Figure 3 Some individuals were not assigned to any subpopulation (indicated as grey dots in the PCA, Figure 4).

| Landscape genetics
Comparing the best models obtained through the optimization process revealed that the IBR, accounting for land cover and the presence of main roads, was by far the best-supported hypothesis (  between 2000 and 3000). However, the highest resistance to wild boar movement was found for urban areas and main roads (resistance values >3000; Table 3). The cumulative current map generated from the resistance surface optimized under the IBR hypothesis is shown in Figure 5.    Sulcis and Iglesiente, west to the SS131 and to Cagliari urban area.
On the other side of the SS131, the two eastern subpopulations, one in the north (Gallura) and center (Barbagia) (NCE), and one in the south, including the area of Sarrabus (SE), showed a weaker genetic divergence between each other. These two areas were included in a single subpopulation by Scandura et al. (2011) and showed a high level of overlap in this study (see Figure 4 and Appendix S2).
IBD, IBB, and IBR were tested to identify environmental and anthropogenic features that might limit gene flow in the Sardinian wild boar population. These analyses revealed that the best-supported hypothesis was the IBR, assigning a relevant ecological role in hindering Sardinian wild boar movements to main roads, urban areas, and intensively cultivated areas. Euclidean distance alone appeared to barely explain genetic distance, thus confirming results of previous studies at a continental (Scandura et al., 2008;Vilaça et al., 2014) or sub-continental scale (Niedziałkowska et al., 2021), while contrasting evidence deriving from a few investigations at a regional scale (Frantz et al., 2009, and Goedbloed et al., 2013, in Central-Western Europe, Alexandri et al., 2017. As discussed by Renner et al. (2016), This result suggests that the Sardinian wild boar might not be so generalist regarding habitat preferences for its moving patterns (Dondina et al., 2019). Particularly, the genetic differentiation between western and eastern wild boar subpopulations seemed to occur in conjunction with the motorway SS131 (Figure 1) As discussed by Reiner et al. (2021), detecting genetic boundaries associable to landscape elements might also help to improve understanding of population connectivity in order to control the potential introduction and spread of diseases transmitted by wild boars. This would be of growing relevance for pathogens such as African swine fever virus, which is transmissible between domestic pigs, wild boar and hybrids, and represents a big threat to the pig economy worldwide (Busch et al., 2021). ASF has been endemic in Sardinia for many years (Jurado et al., 2018), and new outbreaks have been recently recorded in north-western Italy (https://www.reute rs.com/marke ts/commo ditie s/afric an-swine -fever -found -wild-boar-italy -regio nalgover nment -says-2022-01-07/).
According to our data, the Sardinian wild boar population should not be managed as a single panmictic unit, rather subpopulations should be treated as separate management units. The lack of gene flow across barriers (e.g., the SS131 and Campidano plain) should be taken into account in the definition of spatial units for disease prevention. Results may also have implications for the management of other wild species in Sardinia. Given that urban areas, main roads and the most intensively cultivated areas apparently play a role as barriers to gene flow in the wild boar population, they could also represent a cause of fragmentation for other mammals (including endemic and endangered species), promoting isolation and genetic drift. However, the effect on other species should be tested by targeted studies, as landscape features might have various impacts on different species (Renner et al., 2016). Concluding, this study confirms how the joint effect of landscape features can generate genetic discontinuities even across a large mammal population, as already observed in other species such as red deer (Cervus elaphus, Frantz et al., 2012) and bobcats (Lynx rufus, Serieys et al., 2014). Further research would improve knowledge on the role of specific habitat features in preventing an effective dispersal in Sardinian wild boar, although general conclusions about landscape permeability in this species should not be drawn from individual studies (Renner et al., 2016). Finally, possible long-term detrimental effects (small population size, inbreeding, genetic drift) of habitat fragmentation should be carefully evaluated in the Sardinian wild boar, in order to promote a sustainable management of its endemic genetic diversity.

ACK N OWLED G EM ENTS
We are grateful to all people who kindly provided biological samples for genetic analysis, with a special mention to C. Gortázar,

CO N FLI C T O F I NTE R E S T
The authors have no conflicts of interest to declare.